Asset Details
Do you wish to reserve the book?
Machine Learning Approaches to Predict the Hardness of Cast Iron
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Machine Learning Approaches to Predict the Hardness of Cast Iron
Do you wish to request the book?
Machine Learning Approaches to Predict the Hardness of Cast Iron
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Machine Learning Approaches to Predict the Hardness of Cast Iron
2020
Overview
The accurate prediction of the mechanical properties of foundry alloys is a rather complex task given the substantial variability of metallurgical conditions that can be created during casting even in the presence of minimal variations in the constituents and in the process parameters. In this study an application of different intelligent methods of classification, based on the machine learning, to the estimation of the hardness of a traditional spheroidal cast iron and of a less common compact graphite cast iron is proposed. Microstructures are used as inputs to train the neural networks, while hardness is obtained as outputs. As general result, it is possible to admit that ‘light’ open source self-learning algorithms, combined with databases consisting of about 20-30 measures are already able to predict hardness properties with errors below 15 %.
Publisher
University of Kragujevac, Faculty of Engineering
